Transcript ics280_ubicomp_gps - Donald Bren School of Information and

ICS 280 – Autonomous Vehicle Positioning With GPS in Urban Canyon Environments
Autonomous Vehicle Positioning with
GPS in Urban Canyon Environments
By: Youjing Cui and Shuzhi Sam Ge
Presented by: Trung Ngo
Class: ICS 280
Donald Bren School of Information and Computer Sciences
Department of Informatics
University of California, Irvine
Department of Informatics, Bren School ICS, UCI
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ICS 280 – Autonomous Vehicle Positioning With GPS in Urban Canyon Environments
Motivation
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GPS signals in urban canyon environments
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Blocked by high rise buildings
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Not enough available satellite signals
Existing approaches
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Increase the number of visible satellites
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Integrate receivers with sensors
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Inertial navigation system (INS)
Use external references
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Example: GLONASS – Make eight or more satellites available
Such as altimeter or a precise clock
Find a constrained solution:
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In some cases, the altitude can be considered constant and assumed to be known
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ICS 280 – Autonomous Vehicle Positioning With GPS in Urban Canyon Environments
Pseudorange equation
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At least for satellites are needed to solve the
standard pseudo-range equation
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User position: (x, y, z)
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N satellites (i = 1.. N)
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Clock diff.
Actual range
Pseudorange measurement:
Can be
eliminated
with DGPS
pi = f(x, y, z) + Br + vi
4 unknowns -> need 4 equations
Known value
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ICS 280 – Autonomous Vehicle Positioning With GPS in Urban Canyon Environments
New approach
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Observation:
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Most pieces roads are straight lines, arcs, or other
simple smooth curves
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Thus, the user position (x, y, z) can be simply modeled
as (x, f1(x), f2(x)) where f1 and f2 are known based the
road models
In this paper, a new constrained solution is provided
to solve the problem by approximately modeling
the path of vehicle by pieces of curves in the
urban canyon environments
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ICS 280 – Autonomous Vehicle Positioning With GPS in Urban Canyon Environments
New Pseudorange equation
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pi = f(x, y, z) + Br + vi
y = f1(x)
Z = f2(y)
(n equations)
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Total we have n+2 equations
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There are 4 unknown variables, thus with n = 2 we can
solve the problem.
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Two mathematical approaches proposed in the paper for
this, including an extended version of Kalman Filtering
technique (EKF)
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Thus, the minimum number of satellites required drops
to two
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ICS 280 – Autonomous Vehicle Positioning With GPS in Urban Canyon Environments
Road intersection problem
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If user travel in only one road, the problem becomes
simpler.
However, in real urban environments, the road
segments are connected by intersections
–It
is important to know which
road the vehicle takes when
crossing road intersections
Vehicle comes to an intersection
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ICS 280 – Autonomous Vehicle Positioning With GPS in Urban Canyon Environments
Map Representation
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Road segments are connected by intersections
-Road segments can be known based on city
maps
-For each road, the following information need
to be stored
-Road shape (e.g., line, arc …)
-Positions of intersections on the road
-Indexes of roads connected at each
intersection
-Additional parameters of the road model
Roads segment and intersection representation
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ICS 280 – Autonomous Vehicle Positioning With GPS in Urban Canyon Environments
Determine next road at intersections
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In this paper, the interacting multiple model (IMM)
algorithm employed to solve the problem.
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This algorithm has been widely used in multi-target
tracking applications.
Other statistical techniques commonly used in robot
navigation applications can be employed also
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Neighbor algorithm
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Tracking-splitting filter
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Join-likelihood algorithms
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Markov approaches
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ICS 280 – Autonomous Vehicle Positioning With GPS in Urban Canyon Environments
Simulations
- Grey horizontal plane frames represent buildings
- Building heights ranged from 60-180m
- Vehicle travel from point A to point I
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ICS 280 – Autonomous Vehicle Positioning With GPS in Urban Canyon Environments
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Results
Error (m)
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2
1
2
5
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15
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15
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Mean error = 0.436m
Mean error = 1205 m(???)
Department of Informatics, Bren School ICS, UCI
ICS 280 – Autonomous Vehicle Positioning With GPS in Urban Canyon Environments
Conclusion
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A constrained method proposed
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Approximately modeling the path of the vehicle in the
urban canyon environment as pieces of curves
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Helps to reduce the minimum number of available
satellites reduces to two
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